Your users need more than another interface.
They need help with an instrument, a method or a multi-step task. Start from the work they want to complete and the expertise it requires.
Agentic AI / Scientific instruments
Turn instrument expertise into useful AI capabilities.
QPillars builds agentic AI for laboratory instruments and scientific workflows. We connect instrument capabilities, ground agents in specialized knowledge and use executable models where a proposed action needs checking before execution.
ground / Conceptual model
The customer problem
Where the next capability gets blocked.
The knowledge and the tools are separate.
The assistant needs relevant context and access to selected scientific capabilities. Reliable interfaces make those capabilities usable.
Actions need a reviewable path.
For workflows that act on instruments, you need a clear plan, defined checks, approval and an integration that reports the execution outcome.
What we build
A useful scope.
A reviewable result.
Choose the first capability around a specific customer problem. A knowledge assistant, an instrument interface and a modeled workflow can each be useful on their own.
- 01
Connect: instrument and agent interfaces
Expose selected capabilities through a suitable API, SDK adapter, SiLA 2 integration or MCP server. Define operations and their behavior before an agent uses them.
- 02
Ground: custom agents and specialized skills
Bring domain knowledge, procedures and scientific tools into an assistant. Evaluate its usefulness and limits with your subject-matter experts.
- 03
Verify: executable digital twins
Model selected state, resources and constraints to check proposed workflows. Make findings and model coverage visible before the execution decision.
- 04
Build on your instrument software
Integrate the capability into a usable product: interface, backend services, permissions and the connection to the instrument or lab platform.
Relevant engineering
Our liquid-handling demonstration shows how a proposed task can become a plan with modeled checks. Our client case studies cover the underlying instrument-software engineering experience.
Before we start
A few practical questions.
What is agentic AI for laboratory instruments?
An agent uses relevant knowledge and selected tools to work through a task. For laboratory instruments, that can mean answering a technical question, preparing a method or proposing a workflow through supported operations.
Where should we start?
Choose one user group and a task with a clear useful outcome. We assess the available knowledge, interfaces and constraints, then propose the smallest scope that can be evaluated.
Do we need all three layers?
No. A knowledge assistant can be useful without a twin, and an integration can serve conventional lab software without an agent. We combine layers when the task benefits from them.
Can you work with our existing engineering team?
Yes. We agree software responsibilities, collaboration and access to instrument test environments with your team, then deliver a defined capability with review milestones.
Your next project
What’s next for your instrument?
Tell us what you want to connect, simplify or automate. Let’s define the first useful step together.